{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers.convolutional import Conv3D\n",
    "from keras.layers.convolutional_recurrent import ConvLSTM2D\n",
    "from keras.layers.normalization import BatchNormalization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### seq2seq model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "seq = Sequential()\n",
    "seq.add(ConvLSTM2D(filters=1, kernel_size=(2, 2),\n",
    "                   input_shape=(None, 7, 9, 1),\n",
    "                   padding='same', return_sequences=True))\n",
    "seq.add(BatchNormalization())\n",
    "\n",
    "seq.add(ConvLSTM2D(filters=1, kernel_size=(3, 3),\n",
    "                   padding='same', return_sequences=True))\n",
    "seq.add(BatchNormalization())\n",
    "\n",
    "seq.add(Conv3D(filters=1, kernel_size=(3, 3, 3),\n",
    "               activation='sigmoid',\n",
    "               padding='same', data_format='channels_last'))\n",
    "seq.compile(loss='mean_absolute_error', optimizer='sgd')\n",
    "# seq.compile(loss='mean_squared_error', optimizer='sgd', metrics=['mae', 'acc'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## load data\n",
    "We dump the preprocessed data into the Python Pickle files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    import cPickle as pickle\n",
    "except:\n",
    "    import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data//X-1316.pkl', 'rb') as file:\n",
    "    X = bpickle.load(file)\n",
    "with open('data//y-1316-seq.pkl', 'rb') as file1:\n",
    "    Y = pickle.load(file1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35016, 48, 7, 9, 1)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35016, 48, 7, 9, 1)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X[:30635]\n",
    "Y_train = Y[:30635]\n",
    "\n",
    "X_test = X[30635:35015]\n",
    "Y_test = Y[30635:35015]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 29103 samples, validate on 1532 samples\n",
      "Epoch 1/50\n",
      "29103/29103 [==============================] - 1447s 50ms/step - loss: 0.0835 - val_loss: 0.0324\n",
      "Epoch 2/50\n",
      "29103/29103 [==============================] - 1435s 49ms/step - loss: 0.0633 - val_loss: 0.0292\n",
      "Epoch 3/50\n",
      "29103/29103 [==============================] - 1437s 49ms/step - loss: 0.0591 - val_loss: 0.0272\n",
      "Epoch 4/50\n",
      "29103/29103 [==============================] - 1433s 49ms/step - loss: 0.0552 - val_loss: 0.0259\n",
      "Epoch 5/50\n",
      "29103/29103 [==============================] - 1430s 49ms/step - loss: 0.0487 - val_loss: 0.0242\n",
      "Epoch 6/50\n",
      "29103/29103 [==============================] - 1431s 49ms/step - loss: 0.0421 - val_loss: 0.0225\n",
      "Epoch 7/50\n",
      "29103/29103 [==============================] - 1429s 49ms/step - loss: 0.0387 - val_loss: 0.0212\n",
      "Epoch 8/50\n",
      "29103/29103 [==============================] - 1431s 49ms/step - loss: 0.0364 - val_loss: 0.0203\n",
      "Epoch 9/50\n",
      "29103/29103 [==============================] - 1429s 49ms/step - loss: 0.0348 - val_loss: 0.0196\n",
      "Epoch 10/50\n",
      "29103/29103 [==============================] - 1433s 49ms/step - loss: 0.0336 - val_loss: 0.0191\n",
      "Epoch 11/50\n",
      "29103/29103 [==============================] - 1433s 49ms/step - loss: 0.0326 - val_loss: 0.0187\n",
      "Epoch 12/50\n",
      "29103/29103 [==============================] - 1435s 49ms/step - loss: 0.0319 - val_loss: 0.0183\n",
      "Epoch 13/50\n",
      "29103/29103 [==============================] - 1436s 49ms/step - loss: 0.0313 - val_loss: 0.0178\n",
      "Epoch 14/50\n",
      "29103/29103 [==============================] - 1431s 49ms/step - loss: 0.0306 - val_loss: 0.0176\n",
      "Epoch 15/50\n",
      "29103/29103 [==============================] - 1435s 49ms/step - loss: 0.0300 - val_loss: 0.0176\n",
      "Epoch 16/50\n",
      "29103/29103 [==============================] - 1434s 49ms/step - loss: 0.0295 - val_loss: 0.0172\n",
      "Epoch 17/50\n",
      "29103/29103 [==============================] - 1452s 50ms/step - loss: 0.0291 - val_loss: 0.0170\n",
      "Epoch 18/50\n",
      "29103/29103 [==============================] - 1431s 49ms/step - loss: 0.0285 - val_loss: 0.0166\n",
      "Epoch 19/50\n",
      "29103/29103 [==============================] - 1430s 49ms/step - loss: 0.0280 - val_loss: 0.0164\n",
      "Epoch 20/50\n",
      "29103/29103 [==============================] - 1432s 49ms/step - loss: 0.0273 - val_loss: 0.0161\n",
      "Epoch 21/50\n",
      "29103/29103 [==============================] - 1433s 49ms/step - loss: 0.0267 - val_loss: 0.0159\n",
      "Epoch 22/50\n",
      "29103/29103 [==============================] - 1433s 49ms/step - loss: 0.0262 - val_loss: 0.0156\n",
      "Epoch 23/50\n",
      "29103/29103 [==============================] - 1433s 49ms/step - loss: 0.0255 - val_loss: 0.0153\n",
      "Epoch 24/50\n",
      "29103/29103 [==============================] - 1433s 49ms/step - loss: 0.0251 - val_loss: 0.0151\n",
      "Epoch 25/50\n",
      "29103/29103 [==============================] - 1438s 49ms/step - loss: 0.0245 - val_loss: 0.0149\n",
      "Epoch 26/50\n",
      "29103/29103 [==============================] - 1468s 50ms/step - loss: 0.0241 - val_loss: 0.0142\n",
      "Epoch 27/50\n",
      "29103/29103 [==============================] - 1450s 50ms/step - loss: 0.0236 - val_loss: 0.0145\n",
      "Epoch 28/50\n",
      "29103/29103 [==============================] - 1442s 50ms/step - loss: 0.0233 - val_loss: 0.0140\n",
      "Epoch 29/50\n",
      "29103/29103 [==============================] - 1440s 49ms/step - loss: 0.0230 - val_loss: 0.0140\n",
      "Epoch 30/50\n",
      "29103/29103 [==============================] - 1439s 49ms/step - loss: 0.0227 - val_loss: 0.0138\n",
      "Epoch 31/50\n",
      "29103/29103 [==============================] - 1433s 49ms/step - loss: 0.0225 - val_loss: 0.0133\n",
      "Epoch 32/50\n",
      "29103/29103 [==============================] - 1436s 49ms/step - loss: 0.0223 - val_loss: 0.0132\n",
      "Epoch 33/50\n",
      "29103/29103 [==============================] - 1448s 50ms/step - loss: 0.0221 - val_loss: 0.0139\n",
      "Epoch 34/50\n",
      "29103/29103 [==============================] - 1446s 50ms/step - loss: 0.0220 - val_loss: 0.0134\n",
      "Epoch 35/50\n",
      "29103/29103 [==============================] - 1447s 50ms/step - loss: 0.0219 - val_loss: 0.0134\n",
      "Epoch 36/50\n",
      "29103/29103 [==============================] - 1446s 50ms/step - loss: 0.0217 - val_loss: 0.0128\n",
      "Epoch 37/50\n",
      "29103/29103 [==============================] - 1449s 50ms/step - loss: 0.0217 - val_loss: 0.0130\n",
      "Epoch 38/50\n",
      "29103/29103 [==============================] - 1454s 50ms/step - loss: 0.0215 - val_loss: 0.0133\n",
      "Epoch 39/50\n",
      "29103/29103 [==============================] - 1455s 50ms/step - loss: 0.0214 - val_loss: 0.0126\n",
      "Epoch 40/50\n",
      "29103/29103 [==============================] - 1450s 50ms/step - loss: 0.0213 - val_loss: 0.0129\n",
      "Epoch 41/50\n",
      "29103/29103 [==============================] - 1456s 50ms/step - loss: 0.0213 - val_loss: 0.0137\n",
      "Epoch 42/50\n",
      "29103/29103 [==============================] - 1450s 50ms/step - loss: 0.0212 - val_loss: 0.0131\n",
      "Epoch 43/50\n",
      "29103/29103 [==============================] - 1437s 49ms/step - loss: 0.0212 - val_loss: 0.0136\n",
      "Epoch 44/50\n",
      "29103/29103 [==============================] - 1435s 49ms/step - loss: 0.0211 - val_loss: 0.0137\n",
      "Epoch 45/50\n",
      "29103/29103 [==============================] - 1437s 49ms/step - loss: 0.0211 - val_loss: 0.0127\n",
      "Epoch 46/50\n",
      "29103/29103 [==============================] - 1440s 49ms/step - loss: 0.0209 - val_loss: 0.0124\n",
      "Epoch 47/50\n",
      "29103/29103 [==============================] - 1436s 49ms/step - loss: 0.0209 - val_loss: 0.0131\n",
      "Epoch 48/50\n",
      "29103/29103 [==============================] - 1434s 49ms/step - loss: 0.0209 - val_loss: 0.0126\n",
      "Epoch 49/50\n",
      "29103/29103 [==============================] - 1436s 49ms/step - loss: 0.0207 - val_loss: 0.0120\n",
      "Epoch 50/50\n",
      "29103/29103 [==============================] - 1437s 49ms/step - loss: 0.0205 - val_loss: 0.0122\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x29721d56240>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seq.fit(X_train, Y_train, batch_size=10, epochs=50, validation_split=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_hat = seq.predict(X_test, verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## visulization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visulization(y, y_hat, n_slots):\n",
    "    '''\n",
    "    visulize the prpcess of prediction by randomly select n_slots predictions.\n",
    "    y: the labels of test set: shape like (200, 9, 9, 1)\n",
    "    y_hat: the predicted labels: shape like (200, 9, 9, 1)\n",
    "    n_slots: number of frames to be visulized\n",
    "    '''\n",
    "    import random\n",
    "    import matplotlib.pyplot as plt\n",
    "    import numpy as np\n",
    "    length = y_hat.shape[0]\n",
    "    random_ind = random.sample(range(0, length-1), n_slots)\n",
    "    stations = ['station 0', 'station 1', 'station 2', 'station 3', 'station 4', 'station 5', 'station 6', 'station 7']\n",
    "#     fea = ['co', 'no2', 'o3', 'pm10', 'pm2.5', 'so2', 'pressure', 'temperature', 'humidity']\n",
    "    fea = ['co', 'no2', 'o3', 'pm10', 'pm2.5', 'so2', 'pres', 'temp', 'humi']\n",
    "    for ind in random_ind:\n",
    "        \n",
    "        fig, (ax1, ax2)= plt.subplots(1, 2, sharey=True, sharex=True, figsize=(10, 5))\n",
    "        \n",
    "        plt.yticks(np.arange(8), stations, fontsize=12)\n",
    "        ax1.set_yticklabels(stations,fontsize=13)\n",
    "        ax1.set_xticklabels(fea)\n",
    "#         plt.xticks = [0, 1, 2,3,4,5,6,7,8]\n",
    "        plt.setp(ax1, xticks = [0, 1, 2,3,4,5,6,7,8])\n",
    "    \n",
    "        plt.setp(ax1.get_xticklabels(), rotation=30,ha=\"right\", rotation_mode=\"anchor\")\n",
    "        plt.setp(ax2.get_xticklabels(), rotation=30)\n",
    "#         plt.xticks(range(9), ['co', 'no2', 'o3', 'pm10', 'pm2.5', 'so2', 'pres', 'temp', 'humi'], rotation=30)\n",
    "#         ax1 = pyplot.subplot(121)\n",
    "        y_pre = y_hat[ind,23,:,:,0] #visulize the last frame\n",
    "        ax1.imshow(y_pre)\n",
    "#         plt.title('Predition', fontsize=15)\n",
    "        plt.text(-9, -0.8, 'Predition', fontsize=13)\n",
    "#         ax1.xticks(np.arange(9), ['co', 'no2', 'o3', 'pm10', 'pm2.5', 'so2', 'pres', 'temp', 'humi'])\n",
    "#         ax2 = pyplot.subplot(122,sharey=ax1)\n",
    "        y_truth = y[ind,23,:,:,0]\n",
    "        ax2.imshow(y_truth)\n",
    "#         plt.title('Ground truth', fontsize=15)\n",
    "        plt.text(2, -0.8, 'Ground truth', fontsize=13)\n",
    "    plt.savefig('figs//predict-map.pdf', dpi=300, bbox_inches ='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visulization(Y_test, Y_hat, 4)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python(keras-tf)",
   "language": "python",
   "name": "tensorflow"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
